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Adaptive Signal Processing
Master level
No. of credits: 7
Sayed, A.H., Fundamentals of Adaptive Filtering, Wiley, 2003
Haykin, S., Adaptive Filter Theory, 4/E, Prentice-Hall, 2001
J.C. Principe et al., Neural and Adaptive Systems, Wiley, 2000
 Tutorials: Linear adaptive filtering  (Chapter 3 from my DSP book, in Romanian)
Introduction to Artificial Neural Networks
 (Chapter 1 from my ANN book, in Romanian)
General description

The course focuses on advanced topics on adaptive filtering. Main themes are related to linear filtering algorithms in time and frequency domains, nonlinear adaptive filters implemented as neural networks, neural architectures and learning algorithms for feedforward and recurrent networks. Applications include pattern recognition (OCR and face processing), data transmission channel equalization and analog decoding, biomedical time series analysis, system identification. Software support is provided by MATLAB and NeuroSolutions neural networks simulator.

 Course outline
 Lecture 1:
 More info: 1
 Lab 1
General introduction to adaptive linear filtering
     Optimal filtering problem and Wiener solution. Definition and characterization of random processes
     Classification criteria of adaptive algorithms. Cost functions.
     Applications of adaptive filters
 Lecture 2:
 Lab 2
First order adaptive algorithms: gradient descent, Newton algorithm.
 Lecture 3:
 More info: 1, 2, 3
 Lab 3 HW 1
LMS algorithm and its variants.
Adaptive filtering in the frequency domain
 Lecture 4:
 More info: 1
Least-Squares (LS) algorithm. Recurrent Least-Squares (RLS) algorithm.
 Lecture 5:
General introduction to Artificial Neural Networks (ANN's)
     Motivations for studying ANN's
     Definition and classification criteria for ANN's
     Applications of ANN's
 Lecture 6:
Multilayer perceptron (MLP)
     Standard backpropagation training algorithm
     Variants of backpropagation algorithm
 Lecture 7:
Radial Basis Functions (RBF) network
 Lecture 8-9:
Recurrent neural networks
     Analog systems: Hopfield network - theory and applications, Cellular Neural Networks (CNN)
     Discrete systems: Hopfield and Elman networks - theory and applications
 Lecture 10-12:
Applications of ANN's
     Pattern Recognition
     Channel equalization
     Digital filter design